Data-driven models for atmospheric air temperature forecasting at a continental climate region

forecasting temperature was higher than the weekly scale. This may be because the weekly rec- ords were calculated by taking the average temperatures for seven days, which led to the loss of some critical data characteristics. Furthermore, as the weekly scale was derived from the daily records, the length of the time-series data reduced significantly, which affected the efficiency of the model during the training process. This study also investigated the AI models’ capacity to predict temperature when the future pattern data is included. In this scenario, the randomization data division was applied to divide the data into training and testing. The study found that the prediction models’ performance was enhanced after using these techniques. This means that the current pattern of the tempera- ture data affecting climate change influences the quality of predictions. Besides, the case study location starts to be gradually affected by climate change and its impact on temperature values. Thus, this study suggests the following recommendations: • Adopting a robust approach to determine the best input combination instead of existing (ACF and PACF) methods. Fig 12. a) Boxplot of the forecasting error in daily temperature prediction for all proposed models. b) Quantile percent of the forecasting error. https://doi.org/10.1371/journal.pone.0277079.g012 PLOS ONE Air temperature forecasting: Mutli-time scale PLOS ONE | https://doi.org/10.1371/journal.pone.0277079 November 3, 2022 25 / 31